NDLI: Automated method for the detection and segmentation of drusen in colour fundus image for the diagnosis of age-related macular degeneration

Content Provider	IET Digital Library
Author	Mohaimin, Sultan Mohammad Saha, Sajib Kumar Khan, Alve Mahamud Arif, Abu Shamim Mohammad Kanagasingam, Yogesan
Abstract	Age-related macular degeneration (AMD) is one of the main reasons for visual impairment worldwide. The assessment of risk for the development of AMD requires reliable detection and quantitative mapping of retinal abnormalities that are considered as precursors of the disease. Typical signs of the latter are the so-called drusen that appear as yellowish spots in the retina. Automated detection and segmentation of drusen provide vital information about the severity of the disease. The authors propose a novel method for the detection and segmentation of drusen in colour fundus images. The method combines colour information of the object with its boundary information for the accurate detection and segmentation of drusen. To perform non-uniform illumination correction and to minimise inter-subject variability a novel colour normalisation method has been proposed. Experiments are conducted on publicly available STARE and ARIA datasets. The method achieves an overall accuracy of 96.62% which is about 4% higher than the state-of-the-art method. The sensitivity and specificity of the proposed method are 95.96 and 97.64%, respectively.
Starting Page	919
Ending Page	927
Page Count	9
ISSN	17519659
Volume Number	12
e-ISSN	17519667
Issue Number	Issue 6, Jun (2018)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-ipr/12/6
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2017.0685
Journal	IET Image Processing
Publisher Date	2018-01-05
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Age-related Macular Degeneration Diagnosis AMD ARIA Datasets Automated Method Biology And Medical Computing Biomedical Measurement And Imaging Colour Fundus Image Colour Normalisation Method Computer Vision And Image Processing Technique Disease Drusen Detection Drusen Segmentation Eye Health Physics Image Colour Analysis Image Segmentation Inter-subject Variability Minimization Medical And Biomedical Uses of Field Medical Image Processing Nonuniform Illumination Correction Object Detection Optical, Image And Video Signal Processing Patient Diagnostic Method And Instrumentation Quantitative Mapping Radiations Radioactivity Retina Retinal Abnormalities Detection STARE Dataset
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software

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